Time filter

Source Type

Tacoma, WA, United States

Cao L.,University of Technology, Sydney | Cao L.,Cooperative Capital | Zhang H.,Centrelink | Zhao Y.,Centrelink | And 2 more authors.
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics | Year: 2011

Enterprise data mining applications often involve complex data such as multiple large heterogeneous data sources, user preferences, and business impact. In such situations, a single method or one-step mining is often limited in discovering informative knowledge. It would also be very time and space consuming, if not impossible, to join relevant large data sources for mining patterns consisting of multiple aspects of information. It is crucial to develop effective approaches for mining patterns combining necessary information from multiple relevant business lines, catering for real business settings and decision-making actions rather than just providing a single line of patterns. The recent years have seen increasing efforts on mining more informative patterns, e.g., integrating frequent pattern mining with classifications to generate frequent pattern-based classifiers. Rather than presenting a specific algorithm, this paper builds on our existing works and proposes combined mining as a general approach to mining for informative patterns combining components from either multiple data sets or multiple features or by multiple methods on demand. We summarize general frameworks, paradigms, and basic processes for multifeature combined mining, multisource combined mining, and multimethod combined mining. Novel types of combined patterns, such as incremental cluster patterns, can result from such frameworks, which cannot be directly produced by the existing methods. A set of real-world case studies has been conducted to test the frameworks, with some of them briefed in this paper. They identify combined patterns for informing government debt prevention and improving government service objectives, which show the flexibility and instantiation capability of combined mining in discovering informative knowledge in complex data. © 2010 IEEE. Source

Srinivasan U.,Cooperative Capital
IT Professional | Year: 2014

Using several practical examples of cost and quality-of-care outliers, the author presents a framework to detect outliers and anomalies in healthcare services. © 1999-2012 IEEE. Source

Cooperative Capital | Date: 2004-06-01


Liu H.-D.,Nanjing Normal University | Yang M.,Nanjing Normal University | Gao Y.,Nanjing University | Cao L.,University of Technology, Sydney | Cao L.,Cooperative Capital
IEEE Transactions on Circuits and Systems for Video Technology | Year: 2014

Local histogram specification (LHS) is a useful technique for image processing. However, LHS faces a critical computational challenge when it is applied to high-resolution high-precision images. The calculation of the values in the cumulative distribution function (CDF) and the mapped value for the central pixel in each sliding window is time consuming with the computational complexity O(s + L) of the state-of-theart techniques, where s is the side length of the square window and L is the number of gray levels. In this paper, we propose a fast algorithm for LHS, called fast local histogram specification (FLHS). FLHS reduces the complexity of calculating the CDF value for the central pixel in each sliding window to O(s+√L), and the time complexity for the mapping procedure in each window to O(log L). This results in the overall time complexity of LHS reduced from O(s+L) to O(s+√L) in each sliding window. Theoretical analysis shows that the newly developed algorithm is efficient. Experimental results on the 8-bit and high-resolution high-precision (16-bit) images demonstrate the efficiency of our proposed algorithm. © 2014 IEEE. Source

Discover hidden collaborations